Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile

TL;DR

This paper presents a data-driven deep learning approach to predict the emotional quality of car doors based on force profiles during opening, aiding automotive design and user experience enhancement.

Contribution

It introduces a novel deep learning model that correlates force profiles with user-rated affective qualities of car doors, validated across multiple car models.

Findings

High prediction accuracy of the model

Effective generalization to unseen data

Potential applications in automotive design optimization

Abstract

Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designed to capture the underlying relationships between force profiles and user-defined adjective ratings, providing insights into the door-opening experience. The dataset employed in this research includes force profiles and user adjective ratings collected from six distinct car models, reflecting a diverse set of door-opening characteristics and tactile feedback. The model's performance is assessed using Leave-One-Out Cross-Validation, a method that measures its generalization capability on unseen data. The results demonstrate that the proposed model achieves a high level of prediction accuracy, indicating its potential in various applications related to haptic affection and design optimization in the automotive industry.